The use of logic for knowledge representation and reasoning systems is controversial. There are, indeed, several ways that standard First Order Predicate Logic is inappropriate for modelling natural language understanding and commonsense reasoning. However, a more appropriate logic can be designed. This chapter presents several aspects of such a logic.

We propose a logic of belief in which the expansion of beliefs beyond what has been explicitly learned is modeled as a finite computational process. The logic does not impose a particular computational mechanism; rather, the mechanism is a parameter of the logic, and we show that as long as the mechanism meets a particular set of constraints, the resulting logic has certain desirable properties. Chief among these is the property that one can reason soundly about another agent's beliefs by simulating its computational mechanism with one's own. We also give a detailed comparison of our model with Konolige's deduction model, another model of belief in which the believer's reasoning mechanism is a parameter. 2000 Elsevier Science B.V. All rights reserved. Keywords: Belief; Belief inference; Belief ascription; Omniscience; ASK/TELL mechanism; Simulative reasoning; Computational model of belief; Deduction model of belief 1. Introduction If an observer sees dark clouds and hears thunder,...

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Testfpassing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons.

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This essay explores the implications of the thesis that implementation is semantic interpretation. Implementation is (at least) a ternary relation: I is an implementation of an ‘Abstraction ’ A in some medium M. Examples are presented from the arts, from language, from computer science and from cognitive science, where both brains and computers can be understood as implementing a ‘mind Abstraction’. Implementations have side effects due to the implementing medium; these can account for several puzzles surrounding qualia. Finally, an argument for benign panpsychism is developed.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePS-based agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as arguments of propositions and other expressions without leaving first-order logic. The SNePS acting subsystem is integrated with the SNePS reasoning subsystem in such a way that: there are acts that affect what an agent believes; there are acts that specify knowledge-contingent acts and lack-of-knowledge acts; there are policies that serve as “daemons”, triggering acts when certain propositions are believed or wondered about. The GLAIR agent architecture supports metacognition by specifying a location for the source of self-awareness, and of a sense of situatedness in the world. Several SNePS-based agents have taken advantage of these facilities to engage in self-awareness and metacognition.